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Preface to the Second Edition
1. Thinking in Algorithms
Understand the Problem
Naive Solution
Intelligent Approaches
Summary
References
2. The Mathematics of Algorithms
Size of a Problem Instance
Rate of Growth of Functions
Analysis in the Best, Average, and Worst Cases
Performance Families
Benchmark Oraios
References
3. Algorithm Building Blocks
Algorithm Template Format
Pseudocode Template Format
Empirical Evaluation Format
Floating-Point Computation
Example Algorithm
Common Approaches
References
4. Sorting Algorithms
Transposition Sorting
Selection Sort
Heap Sort
Partition-Based Sorting
Sorting without Comparisons
Bucket Sort
Sorting with Extra Storage
String Benchmark Results
Analysis Techniques
References
5. Searching
Sequential Search
Binary Search
Hash-Based Search
Bloom Filter
Binary Search Tree
References
6. Graph Algorithms
Graphs
Depth-First Search
Breadth-First Search
Single-Source Shortest Path
Dijkstras Algorithm for Dense Graphs
Comparing Single-Source Shortest-Path Options
All-Pairs Shortest Path
Minimum Spanning Tree Algorithms
Final Thoughts on Graphs
References
7. Path Finding in AI
Game Trees
Path-Finding Concepts
Minimax
NegMax
AlphaBeta
Search Trees
Depth-First Search
Breadth-First Search
ASearch
Comparing Search-Tree Algorithms
References
8. Network Flow Algorithms
Network Flow
Maximum Flow
Bipartite Matching
Reflections on Augmenting Paths
Minimum Cost Flow
Transshipment
Transportation
Assignment
Linear Programming
References
9. Computational Ge0metry
Classifying Problems
Convex Hull
Convex Hull Scan
Computing Line-Segment Intersections
LineSweep
Voronoi Diagram
References
10. Spatial Tree Structures
Nearest Neior eries
Range eries
Intersection eries
Spatial Tree Structures
Nearest Neior eries
Range ery
dtrees
R-Trees
References
11. Emerging Algorithm Categories
Variations on a Theme
Approximation Algorithms
Parallel Algorithms
Probabilistic Algorithms
References
12. Epilogue: Principles of Algorithms
Know Your Data
Decoe a Problem into Smaller Problems
Choose the Right Data Structure
Make the Space versus Time Trade-Off
Construct a Search
Reduce Your Problem to Another Problem
Writing Algorithms Is Hard-Testing Algorithms Is Harder
Accept Approximate Solutions When Possible
Add Parallelism to Increase Performance
A. Benchmarking
Index
乔治·T·海涅曼,伍斯特理工学院计算机科学系副教授,他还担任了2005年基于组件的软件工程靠前研讨会的议程。
加里·波利斯,伍斯特理工学院实践教授,他还是《Head First Object-0rlented Analysis and Desigrl》一书的合著者。
斯坦利·塞克欧,在将近四十年的时间里,先后在Knoxville伍斯特、蒙特利尔、重庆、洛桑以及巴黎的数所大学中任教。
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